Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

TL;DR

This paper introduces a machine learning framework that infers physical parameters from radiographic data in high energy density physics, enabling physics-consistent inverse modeling for ICF capsule implosions.

Contribution

The work develops a two-stage ML pipeline that directly infers system parameters from radiographs and demonstrates physics consistency and EOS invariance in the predictions.

Findings

Parameters inferred are consistent with hydrodynamics simulations.

The method successfully maps unknown EOS features to analytical EOS parameters.

The approach enables physics-informed inverse modeling from radiographic data.

Abstract

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the estimated parameters can be used in a hydrodynamics code to obtain density fields and hydrodynamic shock and outer edge features that are consistent with the data. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model.